Color imaging processing pipelines typically include a sensor with a color filter array (CFA). During processing, color correction, white balancing, and other processing steps can cause the sensor to have an unequal sensitivity on different channels, which requires analog, and in some cases digital, gains of the sensor to be adjusted. In addition, color interpolation (e.g., when sensor channels are correlated), can give rise to what it is referred to as chrominance noise. Chrominance noise appears as low frequency colored blotches throughout an image, especially in darker flat areas. The effect is more pronounced in lower light levels where the characteristic features are observed as irregularly shaped clusters of colored pixels that can vary anywhere from 15 to 25 pixels across for example.
Although a spatially adaptive color correction matrix can reduce chrominance noise, post processing an image remains problematic. Various image de-noising methods have been developed ranging from bilateral filtering, anisotropic diffusion, and more wavelet coefficient thresholding or shrinkage and de-noising in fractal frameworks. However, each of these methods have undesirable drawbacks.